Imported from borghei/claude-skills (
engineering/chaos-engineering/SKILL.md). Install upstream withnpx skills add borghei/claude-skills --skill chaos-engineering. Copyright stays with the author (MIT + Commons Clause).
Chaos Engineering
End-to-end chaos engineering: experiment design, fault injection catalog, gameday execution, and the maturity model that turns one-off "let's break stuff" exercises into a reliable discipline. Provider-agnostic — works whether you use Litmus, Chaos Mesh, AWS FIS, Gremlin, ChaosToolkit, or hand-rolled scripts.
This skill answers four questions: what to inject, where to inject it, how to size the blast, and how to extract durable learning from each run.
Core Capabilities
- Principles & maturity — the five Principles of Chaos and a four-level maturity model (L0 none → L4 always-on production chaos) with level-up criteria.
- Experiment design loop — a nine-step loop (steady state → hypothesis → variables → blast radius → abort → run → analyze → act → document) with worked good/bad examples.
- Fault catalog — what to inject per layer: pod/host, network, dependency, resource, state, and traffic, with tool mappings.
- Blast-radius sizing — quantify worst-case affected users and recommended caps; experiments start tiny (1 pod / 1% / 1 min) and grow only after passing.
- Gameday execution — scheduled multi-scenario exercises with roles, agendas, scenario selection, and debrief templates.
- Discipline — anti-patterns to avoid, the "first five experiments" for new teams, and end-to-end workflows (single experiment, gameday, kill-switch verification, post-incident verification).
When to Use
| Situation | Skill applies |
|---|---|
| Spinning up a chaos program from scratch | Yes — start with maturity model + first 5 experiments |
| Designing a single experiment for a known concern | Yes — use the experiment design loop |
| Planning a gameday for a team or service | Yes — use scripts/gameday_planner.py |
| Validating a kill switch or fallback path actually works | Yes — chaos is the way to test these in prod-like conditions |
| Post-incident verification: "did the fix really fix it?" | Yes — re-inject the original fault, confirm the new behavior |
| Compliance evidence (SOC 2 A1 / DORA Art. 25) | Yes — chaos runs produce auditable resilience-testing evidence |
| Improving SLOs / error budgets | Pair with engineering/observability-designer — chaos surfaces SLO violations |
Clarify First
Before designing the experiment, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Target & fault type — the service and what to inject (dependency-timeout, network, pod-kill, resource exhaustion) (drives the experiment doc via
--target/--fault) - Steady-state hypothesis — the metric that defines "healthy" and the expected behavior under fault (the hypothesis the experiment tests)
- Blast radius caps — user count, % targeted, duration, and abort triggers (sizes the experiment via
blast_radius_calculator.py)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
| Tool | Purpose | Command |
|---|---|---|
chaos_experiment_designer.py |
Scaffold a chaos experiment doc (hypothesis stub, steady-state template, abort criteria, checklist) | python scripts/chaos_experiment_designer.py --target payments-svc --fault dependency-timeout --duration 5m |
blast_radius_calculator.py |
Compute worst-case affected users, recommended caps, and abort triggers | python scripts/blast_radius_calculator.py --users 100000 --percent-targeted 1 --duration 60 |
gameday_planner.py |
Generate a tailored gameday agenda with roles, timeline, and debrief template | python scripts/gameday_planner.py --service search-api --duration full --scenarios region-failover,dep-outage |
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/experiment-design-loop.md — the five principles, the four-level maturity model, the full nine-step design loop with worked steady-state/hypothesis/abort examples, and the "first five experiments." Read when designing or running an experiment.
- references/gameday-workflows-and-antipatterns.md — the gameday agenda and roles, scenario selection, the chaos anti-patterns, all four end-to-end workflows, and the script tooling-output table. Read when planning a gameday or running a workflow.
- references/chaos-principles-and-maturity.md — per-level maturity scorecard, the L0→L1 "first five experiments" list, and the org/SRE prerequisites for each level. Read when assessing or leveling up a program.
- references/gameday-playbook.md — 12 scenario templates by domain (API service, database, frontend, async pipeline, multi-region, etc.), half-day/full-day agendas, and post-gameday writeup templates. Read when choosing gameday scenarios.
- references/fault-injection-catalog.md — fault types per layer (network, host, dependency, resource, state, traffic) with tool mappings (Chaos Mesh / Litmus / AWS FIS / Gremlin / ChaosToolkit). Read when choosing what to inject.
Related skills
engineering/observability-designer— wire metrics needed to define steady stateengineering/incident-commander— chaos is rehearsal for the incident response you'll needengineering/feature-flags-architect— kill switches verified by chaos; chaos verified by flagsra-qm-team/dora-compliance-expert— DORA Article 25 requires resilience testing; chaos runs are evidence
